/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    AddCluster.java
 *    Copyright (C) 2002-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.filters.unsupervised.attribute;

import java.io.File;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.ObjectInputStream;
import java.util.ArrayList;
import java.util.Enumeration;
import java.util.Vector;

import weka.clusterers.AbstractClusterer;
import weka.clusterers.Clusterer;
import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Range;
import weka.core.SerializationHelper;
import weka.core.SparseInstance;
import weka.core.Utils;
import weka.core.WeightedAttributesHandler;
import weka.core.WeightedInstancesHandler;
import weka.core.WekaException;
import weka.filters.Filter;
import weka.filters.UnsupervisedFilter;

/**
 * <!-- globalinfo-start --> A filter that adds a new nominal attribute
 * representing the cluster assigned to each instance by the specified
 * clustering algorithm.<br/>
 * Either the clustering algorithm gets built with the first batch of data or
 * one specifies are serialized clusterer model file to use instead.
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -W &lt;clusterer specification&gt;
 *  Full class name of clusterer to use, followed
 *  by scheme options. eg:
 *   "weka.clusterers.SimpleKMeans -N 3"
 *  (default: weka.clusterers.SimpleKMeans)
 * </pre>
 * 
 * <pre>
 * -serialized &lt;file&gt;
 *  Instead of building a clusterer on the data, one can also provide
 *  a serialized model and use that for adding the clusters.
 * </pre>
 * 
 * <pre>
 * -I &lt;att1,att2-att4,...&gt;
 *  The range of attributes the clusterer should ignore.
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision$
 */
public class AddCluster extends Filter implements UnsupervisedFilter, OptionHandler, WeightedAttributesHandler, WeightedInstancesHandler {

    /** for serialization. */
    static final long serialVersionUID = 7414280611943807337L;

    /** The clusterer used to do the cleansing. */
    protected Clusterer m_Clusterer = new weka.clusterers.SimpleKMeans();

    /** The file from which to load a serialized clusterer. */
    protected File m_SerializedClustererFile = new File(System.getProperty("user.dir"));

    /** The actual clusterer used to do the clustering. */
    protected Clusterer m_ActualClusterer = null;

    /** Range of attributes to ignore. */
    protected Range m_IgnoreAttributesRange = null;

    /** Filter for removing attributes. */
    protected Filter m_removeAttributes = new Remove();

    /**
     * Returns the Capabilities of this filter, makes sure that the class is never
     * set (for the clusterer).
     * 
     * @param data the data to use for customization
     * @return the capabilities of this object, based on the data
     * @see #getCapabilities()
     */
    @Override
    public Capabilities getCapabilities(Instances data) {
        Instances newData;

        newData = new Instances(data, 0);
        newData.setClassIndex(-1);

        return super.getCapabilities(newData);
    }

    /**
     * Returns the Capabilities of this filter.
     * 
     * @return the capabilities of this object
     * @see Capabilities
     */
    @Override
    public Capabilities getCapabilities() {
        Capabilities result = m_Clusterer.getCapabilities();
        result.enableAllClasses();

        result.setMinimumNumberInstances(0);

        return result;
    }

    /**
     * tests the data whether the filter can actually handle it.
     * 
     * @param instanceInfo the data to test
     * @throws Exception if the test fails
     */
    @Override
    protected void testInputFormat(Instances instanceInfo) throws Exception {
        getCapabilities(instanceInfo).testWithFail(removeIgnored(instanceInfo));
    }

    /**
     * Sets the format of the input instances.
     * 
     * @param instanceInfo an Instances object containing the input instance
     *                     structure (any instances contained in the object are
     *                     ignored - only the structure is required).
     * @return true if the outputFormat may be collected immediately
     * @throws Exception if the inputFormat can't be set successfully
     */
    @Override
    public boolean setInputFormat(Instances instanceInfo) throws Exception {
        super.setInputFormat(instanceInfo);

        m_removeAttributes = null;

        return false;
    }

    /**
     * filters all attributes that should be ignored.
     * 
     * @param data the data to filter
     * @return the filtered data
     * @throws Exception if filtering fails
     */
    protected Instances removeIgnored(Instances data) throws Exception {
        Instances result = data;

        if (m_IgnoreAttributesRange != null || data.classIndex() >= 0) {
            m_removeAttributes = new Remove();
            String rangeString = "";
            if (m_IgnoreAttributesRange != null) {
                rangeString += m_IgnoreAttributesRange.getRanges();
            }
            if (data.classIndex() >= 0) {
                if (rangeString.length() > 0) {
                    rangeString += "," + (data.classIndex() + 1);
                } else {
                    rangeString = "" + (data.classIndex() + 1);
                }
            }
            ((Remove) m_removeAttributes).setAttributeIndices(rangeString);
            ((Remove) m_removeAttributes).setInvertSelection(false);
            m_removeAttributes.setInputFormat(data);
            result = Filter.useFilter(data, m_removeAttributes);
        }

        return result;
    }

    /**
     * Signify that this batch of input to the filter is finished.
     * 
     * @return true if there are instances pending output
     * @throws IllegalStateException if no input structure has been defined
     */
    @Override
    public boolean batchFinished() throws Exception {
        if (getInputFormat() == null) {
            throw new IllegalStateException("No input instance format defined");
        }

        Instances toFilter = getInputFormat();

        if (!isFirstBatchDone()) {
            // serialized model or build clusterer from scratch?
            File file = getSerializedClustererFile();
            if (!file.isDirectory()) {
                int[] attsToIgnore = null;
                ObjectInputStream ois = // new ObjectInputStream(new FileInputStream(file));
                        SerializationHelper.getObjectInputStream(new FileInputStream(file));
                m_ActualClusterer = (Clusterer) ois.readObject();
                Instances header = null;
                // let's see whether there's an Instances header stored as well
                try {
                    header = (Instances) ois.readObject();
                    // ignored atts
                    attsToIgnore = (int[]) ois.readObject();
                } catch (Exception e) {
                    // ignored
                }
                ois.close();

                if (attsToIgnore != null && attsToIgnore.length > 0) {
                    m_removeAttributes = new Remove();
                    ((Remove) m_removeAttributes).setAttributeIndicesArray(attsToIgnore);
                    ((Remove) m_removeAttributes).setInvertSelection(false);
                    m_removeAttributes.setInputFormat(toFilter);
                }

                // same dataset format?
                if ((header != null) && (!header.equalHeaders(toFilter))) {
                    throw new WekaException("Training header of clusterer and filter dataset don't match:\n" + header.equalHeadersMsg(toFilter));
                }
            } else {
                // filter out attributes if necessary
                Instances toFilterIgnoringAttributes = removeIgnored(toFilter);

                m_ActualClusterer = AbstractClusterer.makeCopy(m_Clusterer);
                m_ActualClusterer.buildClusterer(toFilterIgnoringAttributes);
            }

            // create output dataset with new attribute
            Instances filtered = new Instances(toFilter, 0);
            ArrayList<String> nominal_values = new ArrayList<String>(m_ActualClusterer.numberOfClusters());
            for (int i = 0; i < m_ActualClusterer.numberOfClusters(); i++) {
                nominal_values.add("cluster" + (i + 1));
            }
            filtered.insertAttributeAt(new Attribute("cluster", nominal_values), filtered.numAttributes());

            setOutputFormat(filtered);
        }

        // build new dataset
        for (int i = 0; i < toFilter.numInstances(); i++) {
            convertInstance(toFilter.instance(i));
        }

        flushInput();
        m_NewBatch = true;
        m_FirstBatchDone = true;

        return (numPendingOutput() != 0);
    }

    /**
     * Input an instance for filtering. Ordinarily the instance is processed and
     * made available for output immediately. Some filters require all instances be
     * read before producing output.
     *
     * @param instance the input instance
     * @return true if the filtered instance may now be collected with output().
     * @throws IllegalStateException if no input format has been defined.
     */
    @Override
    public boolean input(Instance instance) throws Exception {
        if (getInputFormat() == null) {
            throw new IllegalStateException("No input instance format defined");
        }

        if (m_NewBatch) {
            resetQueue();
            m_NewBatch = false;
        }

        if (outputFormatPeek() != null) {
            convertInstance(instance);
            return true;
        }

        bufferInput(instance);
        return false;
    }

    /**
     * Convert a single instance over. The converted instance is added to the end of
     * the output queue.
     *
     * @param instance the instance to convert
     * @throws Exception if something goes wrong
     */
    protected void convertInstance(Instance instance) throws Exception {
        Instance original, processed;
        original = instance;

        // copy values
        double[] instanceVals = new double[instance.numAttributes() + 1];
        for (int j = 0; j < instance.numAttributes(); j++) {
            instanceVals[j] = original.value(j);
        }
        Instance filteredI = null;
        if (m_removeAttributes != null) {
            m_removeAttributes.input(instance);
            filteredI = m_removeAttributes.output();
        } else {
            filteredI = instance;
        }

        // add cluster to end
        try {
            instanceVals[instance.numAttributes()] = m_ActualClusterer.clusterInstance(filteredI);
        } catch (Exception e) {
            // clusterer couldn't cluster instance -> missing
            instanceVals[instance.numAttributes()] = Utils.missingValue();
        }

        // create new instance
        if (original instanceof SparseInstance) {
            processed = new SparseInstance(original.weight(), instanceVals);
        } else {
            processed = new DenseInstance(original.weight(), instanceVals);
        }

        copyValues(processed, false, instance.dataset(), outputFormatPeek());

        push(processed); // No need to copy instance
    }

    /**
     * Returns an enumeration describing the available options.
     * 
     * @return an enumeration of all the available options.
     */
    @Override
    public Enumeration<Option> listOptions() {
        Vector<Option> result = new Vector<Option>(3);

        result.addElement(new Option("\tFull class name of clusterer to use, followed\n" + "\tby scheme options. eg:\n" + "\t\t\"weka.clusterers.SimpleKMeans -N 3\"\n" + "\t(default: weka.clusterers.SimpleKMeans)", "W", 1, "-W <clusterer specification>"));

        result.addElement(new Option("\tInstead of building a clusterer on the data, one can also provide\n" + "\ta serialized model and use that for adding the clusters.", "serialized", 1, "-serialized <file>"));

        result.addElement(new Option("\tThe range of attributes the clusterer should ignore.\n", "I", 1, "-I <att1,att2-att4,...>"));

        return result.elements();
    }

    /**
     * Parses a given list of options.
     * <p/>
     * 
     * <!-- options-start --> Valid options are:
     * <p/>
     * 
     * <pre>
     * -W &lt;clusterer specification&gt;
     *  Full class name of clusterer to use, followed
     *  by scheme options. eg:
     *   "weka.clusterers.SimpleKMeans -N 3"
     *  (default: weka.clusterers.SimpleKMeans)
     * </pre>
     * 
     * <pre>
     * -serialized &lt;file&gt;
     *  Instead of building a clusterer on the data, one can also provide
     *  a serialized model and use that for adding the clusters.
     * </pre>
     * 
     * <pre>
     * -I &lt;att1,att2-att4,...&gt;
     *  The range of attributes the clusterer should ignore.
     * </pre>
     * 
     * <!-- options-end -->
     * 
     * @param options the list of options as an array of strings
     * @throws Exception if an option is not supported
     */
    @Override
    public void setOptions(String[] options) throws Exception {
        String tmpStr;
        String[] tmpOptions;
        File file;
        boolean serializedModel;

        serializedModel = false;
        tmpStr = Utils.getOption("serialized", options);
        if (tmpStr.length() != 0) {
            file = new File(tmpStr);
            if (!file.exists()) {
                throw new FileNotFoundException("File '" + file.getAbsolutePath() + "' not found!");
            }
            if (file.isDirectory()) {
                throw new FileNotFoundException("'" + file.getAbsolutePath() + "' points to a directory not a file!");
            }
            setSerializedClustererFile(file);
            serializedModel = true;
        } else {
            setSerializedClustererFile(null);
        }

        if (!serializedModel) {
            tmpStr = Utils.getOption('W', options);
            if (tmpStr.length() == 0) {
                tmpStr = weka.clusterers.SimpleKMeans.class.getName();
            }
            tmpOptions = Utils.splitOptions(tmpStr);
            if (tmpOptions.length == 0) {
                throw new Exception("Invalid clusterer specification string");
            }
            tmpStr = tmpOptions[0];
            tmpOptions[0] = "";
            setClusterer(AbstractClusterer.forName(tmpStr, tmpOptions));
        }

        setIgnoredAttributeIndices(Utils.getOption('I', options));

        Utils.checkForRemainingOptions(options);
    }

    /**
     * Gets the current settings of the filter.
     * 
     * @return an array of strings suitable for passing to setOptions
     */
    @Override
    public String[] getOptions() {
        Vector<String> result;
        File file;

        result = new Vector<String>();

        file = getSerializedClustererFile();
        if ((file != null) && (!file.isDirectory())) {
            result.add("-serialized");
            result.add(file.getAbsolutePath());
        } else {
            result.add("-W");
            result.add(getClustererSpec());
        }

        if (!getIgnoredAttributeIndices().equals("")) {
            result.add("-I");
            result.add(getIgnoredAttributeIndices());
        }

        return result.toArray(new String[result.size()]);
    }

    /**
     * Returns a string describing this filter.
     * 
     * @return a description of the filter suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String globalInfo() {
        return "A filter that adds a new nominal attribute representing the cluster " + "assigned to each instance by the specified clustering algorithm.\n" + "Either the clustering algorithm gets built with the first batch of " + "data or one specifies are serialized clusterer model file to use " + "instead.";
    }

    /**
     * Returns the tip text for this property.
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String clustererTipText() {
        return "The clusterer to assign clusters with.";
    }

    /**
     * Sets the clusterer to assign clusters with.
     * 
     * @param clusterer The clusterer to be used (with its options set).
     */
    public void setClusterer(Clusterer clusterer) {
        m_Clusterer = clusterer;
    }

    /**
     * Gets the clusterer used by the filter.
     * 
     * @return The clusterer being used.
     */
    public Clusterer getClusterer() {
        return m_Clusterer;
    }

    /**
     * Gets the clusterer specification string, which contains the class name of the
     * clusterer and any options to the clusterer.
     * 
     * @return the clusterer string.
     */
    protected String getClustererSpec() {
        Clusterer c = getClusterer();
        if (c instanceof OptionHandler) {
            return c.getClass().getName() + " " + Utils.joinOptions(((OptionHandler) c).getOptions());
        }
        return c.getClass().getName();
    }

    /**
     * Returns the tip text for this property.
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String ignoredAttributeIndicesTipText() {
        return "The range of attributes to be ignored by the clusterer. eg: first-3,5,9-last";
    }

    /**
     * Gets ranges of attributes to be ignored.
     * 
     * @return a string containing a comma-separated list of ranges
     */
    public String getIgnoredAttributeIndices() {
        if (m_IgnoreAttributesRange == null) {
            return "";
        } else {
            return m_IgnoreAttributesRange.getRanges();
        }
    }

    /**
     * Sets the ranges of attributes to be ignored. If provided string is null, no
     * attributes will be ignored.
     * 
     * @param rangeList a string representing the list of attributes. eg:
     *                  first-3,5,6-last
     * @throws IllegalArgumentException if an invalid range list is supplied
     */
    public void setIgnoredAttributeIndices(String rangeList) {
        if ((rangeList == null) || (rangeList.length() == 0)) {
            m_IgnoreAttributesRange = null;
        } else {
            m_IgnoreAttributesRange = new Range();
            m_IgnoreAttributesRange.setRanges(rangeList);
        }
    }

    /**
     * Gets the file pointing to a serialized, built clusterer. If it is null or
     * pointing to a directory it will not be used.
     * 
     * @return the file the serialized, built clusterer is located in
     */
    public File getSerializedClustererFile() {
        return m_SerializedClustererFile;
    }

    /**
     * Sets the file pointing to a serialized, built clusterer. If the argument is
     * null, doesn't exist or pointing to a directory, then the value is ignored.
     * 
     * @param value the file pointing to the serialized, built clusterer
     */
    public void setSerializedClustererFile(File value) {
        if ((value == null) || (!value.exists())) {
            value = new File(System.getProperty("user.dir"));
        }

        m_SerializedClustererFile = value;
    }

    /**
     * Returns the tip text for this property.
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String serializedClustererFileTipText() {
        return "A file containing the serialized model of a built clusterer.";
    }

    /**
     * Main method for testing this class.
     * 
     * @param argv should contain arguments to the filter: use -h for help
     */
    public static void main(String[] argv) {
        runFilter(new AddCluster(), argv);
    }
}
